hypergeometric1F1

Compute the Confluent Hypergeometric function: 1F1(a,b,c,t) =
Gamma(b)/(Gamma(b-a)Gamma(a)) Int_0^1 t^(a-1) (1 - t)^(b-a-1) exp(c t) dt

math

Package for Bayesian Variable Selection and Model Averaging
in linear models and generalized linear models using stochastic or
deterministic sampling without replacement from posterior
distributions. Prior distributions on coefficients are
from Zellner's g-prior or mixtures of g-priors
corresponding to the Zellner-Siow Cauchy Priors or the
mixture of g-priors from Liang et al (2008)
<DOI:10.1198/016214507000001337>
for linear models or mixtures of g-priors from Li and Clyde
(2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models.
Other model selection criteria include AIC, BIC and Empirical Bayes
estimates of g. Sampling probabilities may be updated based on the sampled
models using sampling w/out replacement or an efficient MCMC algorithm which
samples models using a tree structure of the model space
as an efficient hash table. See Clyde, Ghosh and Littman (2010)
<DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms.
Uniform priors over all models or beta-binomial prior distributions on
model size are allowed, and for large p truncated priors on the model
space may be used to enforce sampling models that are full rank.
The user may force variables to always be included in addition to imposing
constraints that higher order interactions are included only if their
parents are included in the model.
This material is based upon work supported by the National Science
Foundation under Division of Mathematical Sciences grant 1106891.
Any opinions, findings, and
conclusions or recommendations expressed in this material are those of
the author(s) and do not necessarily reflect the views of the
National Science Foundation.

Merlise Clyde

Bayesian Variable Selection and Model Averaging using Bayesian
Adaptive Sampling

Michael Littman

Joyee Ghosh

Yingbo Li

Betsy Bersson

Don van de Bergh

Quanli Wang

hypergeometric1F1 function

<dl><dt>a</dt>
<dd>arbitrary</dd>
<dt>b</dt>
<dd>Must be greater 0</dd>
<dt>c</dt>
<dd>arbitrary</dd>
<dt>laplace</dt>
<dd>The default is to use the Cephes library; for large a or s
this may return an NA, Inf or negative values,, in which case you should use
the Laplace approximation.</dd>
<dt>log</dt>
<dd>if TRUE, return log(1F1)</dd></dl>

Arguments

Merlise Clyde (<a href="/link/clyde%40stat.duke.edu?package=BAS&version=1.7.1" data-mini-rdoc="BAS::clyde@stat.duke.edu">clyde@stat.duke.edu</a>)

Author

Confluent hypergeometric1F1 function — hypergeometric1F1

<dl>

<dt>a</dt>
<dd>arbitrary</dd>


<dt>b</dt>
<dd>Must be greater 0</dd>


<dt>c</dt>
<dd>arbitrary</dd>


<dt>laplace</dt>
<dd>The default is to use the Cephes library; for large a or s
this may return an NA, Inf or negative values,, in which case you should use
the Laplace approximation.</dd>


<dt>log</dt>
<dd>if TRUE, return log(1F1)</dd>

</dl>

Merlise Clyde (<a href='mailto:clyde@stat.duke.edu'>clyde@stat.duke.edu</a>)

hypergeometric1F1: Confluent hypergeometric1F1 function

Description

Usage

Arguments

Author

References

See Also

Examples